OpenRaiser/Intern-Atlas huggingface dataset:
CAST: Cross-Attentive Spatio-Temporal feature fusion for Deepfake detection
Venue: arXiv cs.CV 2025
Year: 2025

Deepfakes have emerged as a significant threat to digital media authenticity, increasing the need for advanced detection techniques that can identify subtle and time-dependent manipulations. CNNs are effective at capturing spatial artifacts, and Transformers excel at modeling temporal inconsistencies. However, many existing CNN-Transformer models process spatial and temporal features independently. In particular, attention-based methods often use separate attention mechanisms for spatial and temporal features and combine them using naive approaches like averaging, addition, or concatenation, which limits the depth of spatio-temporal interaction. To address this challenge, we propose a unified CAST model that leverages cross-attention to effectively fuse spatial and temporal features in a more integrated manner. Our approach allows temporal features to dynamically attend to relevant spatial regions, enhancing the model's ability to detect fine-grained, time-evolving artifacts such as flickering eyes or warped lips. This design enables more precise localization and deeper contextual understanding, leading to improved performance across diverse and challenging scenarios. We evaluate the performance of our model using the FaceForensics++, Celeb-DF, and DeepfakeDetection datasets in both intra- and cross-dataset settings to affirm the superiority of our approach. Our model achieves strong performance with an AUC of 99.49 percent and an accuracy of 97.57 percent in intra-dataset evaluations. In cross-dataset testing, it demonstrates impressive generalization by achieving a 93.31 percent AUC on the unseen DeepfakeDetection dataset. These results highlight the effectiveness of cross-attention-based feature fusion in enhancing the robustness of deepfake video detection.
Performance Prediction for Large Systems via Text-to-Text Regression
Venue: arXiv cs.LG 2025
Year: 2025

In many industries, predicting metric outcomes of large systems is a fundamental problem, driven largely by traditional tabular regression. However, such methods struggle on complex systems data in the wild such as configuration files or system logs, where feature engineering is often infeasible. We propose text-to-text regression as a general, scalable alternative. For predicting resource efficiency on Borg, Google's massive compute cluster scheduling system, a 60M parameter encoder-decoder, trained from random initialization, achieves up to a near perfect 0.99 (0.9 average) rank correlation across the entire fleet, and 100x lower MSE than tabular approaches. The model also easily adapts to new tasks in only 500 few-shot examples and captures the densities of complex outcome distributions. Ablation studies highlight the importance of using encoders, increasing sequence length, and the model's inherent uncertainty quantification. These findings pave the way for universal simulators of real-world outcomes.
Equitable Federated Learning with NCA
Venue: arXiv cs.CV 2025
Year: 2025

Federated Learning (FL) is enabling collaborative model training across institutions without sharing sensitive patient data. This approach is particularly valuable in low- and middle-income countries (LMICs), where access to trained medical professionals is limited. However, FL adoption in LMICs faces significant barriers, including limited high-performance computing resources and unreliable internet connectivity. To address these challenges, we introduce FedNCA, a novel FL system tailored for medical image segmentation tasks. FedNCA leverages the lightweight Med-NCA architecture, enabling training on low-cost edge devices, such as widely available smartphones, while minimizing communication costs. Additionally, our encryption-ready FedNCA proves to be suitable for compromised network communication. By overcoming infrastructural and security challenges, FedNCA paves the way for inclusive, efficient, lightweight, and encryption-ready medical imaging solutions, fostering equitable healthcare advancements in resource-constrained regions.
(Fact) Check Your Bias
Venue: arXiv cs.CL 2025
Year: 2025

Automatic fact verification systems increasingly rely on large language models (LLMs). We investigate how parametric knowledge biases in these models affect fact-checking outcomes of the HerO system (baseline for FEVER-25). We examine how the system is affected by: (1) potential bias in Llama 3.1's parametric knowledge and (2) intentionally injected bias. When prompted directly to perform fact-verification, Llama 3.1 labels nearly half the claims as "Not Enough Evidence". Using only its parametric knowledge it is able to reach a verdict on the remaining half of the claims. In the second experiment, we prompt the model to generate supporting, refuting, or neutral fact-checking documents. These prompts significantly influence retrieval outcomes, with approximately 50\% of retrieved evidence being unique to each perspective. Notably, the model sometimes refuses to generate supporting documents for claims it believes to be false, creating an inherent negative bias. Despite differences in retrieved evidence, final verdict predictions show stability across prompting strategies. The code is available at: https://github.com/eibakke/FEVER-8-Shared-Task
ViStruct: Simulating Expert-Like Reasoning Through Task Decomposition and Visual Attention Cues
Venue: arXiv cs.HC 2025
Year: 2025

Data visualization tasks often require multi-step reasoning, and the interpretive strategies experts use, such as decomposing complex goals into smaller subtasks and selectively attending to key chart regions are rarely made explicit. ViStruct is an automated pipeline that simulates these expert behaviours by breaking high-level visual questions into structured analytic steps and highlighting semantically relevant chart areas. Leveraging large language and vision-language models, ViStruct identifies chart components, maps subtasks to spatial regions, and presents visual attention cues to externalize expert-like reasoning flows. While not designed for direct novice instruction, ViStruct provides a replicable model of expert interpretation that can inform the development of future visual literacy tools. We evaluate the system on 45 tasks across 12 chart types and validate its outputs with trained visualization users, confirming its ability to produce interpretable and expert-aligned reasoning sequences.
THE-Tree: Can Tracing Historical Evolution Enhance Scientific Verification and Reasoning?
Venue: arXiv cs.AI 2025
Year: 2025

Large Language Models (LLMs) are accelerating scientific idea generation, but rigorously evaluating these numerous, often superficial, AI-generated propositions for novelty and factual accuracy is a critical bottleneck; manual verification is too slow. Existing validation methods are inadequate: LLMs as standalone verifiers may hallucinate and lack domain knowledge (our findings show 60% unawareness of relevant papers in specific domains), while traditional citation networks lack explicit causality and narrative surveys are unstructured. This underscores a core challenge: the absence of structured, verifiable, and causally-linked historical data of scientific evolution.To address this,we introduce \textbf{THE-Tree} (\textbf{T}echnology \textbf{H}istory \textbf{E}volution Tree), a computational framework that constructs such domain-specific evolution trees from scientific literature. THE-Tree employs a search algorithm to explore evolutionary paths. During its node expansion, it utilizes a novel "Think-Verbalize-Cite-Verify" process: an LLM proposes potential advancements and cites supporting literature. Critically, each proposed evolutionary link is then validated for logical coherence and evidential support by a recovered natural language inference mechanism that interrogates the cited literature, ensuring that each step is grounded. We construct and validate 88 THE-Trees across diverse domains and release a benchmark dataset including up to 71k fact verifications covering 27k papers to foster further research. Experiments demonstrate that i) in graph completion, our THE-Tree improves hit@1 by 8% to 14% across multiple models compared to traditional citation networks; ii) for predicting future scientific developments, it improves hit@1 metric by nearly 10%; and iii) when combined with other methods, it boosts the performance of evaluating important scientific papers by almost 100%.
Evaluating List Construction and Temporal Understanding capabilities of Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) have demonstrated immense advances in a wide range of natural language tasks. However, these models are susceptible to hallucinations and errors on particularly temporal understanding tasks involving multiple entities in answers. In such tasks, they fail to associate entities with accurate time intervals, generate a complete list of entities in answers or reason about events associated with specific temporal bounds. Existing works do not extensively evaluate the abilities of the model to perform implicit and explicit temporal understanding in a list answer construction setup. To bridge this gap, we propose the Time referenced List based Question Answering or TLQA benchmark that requires structured answers in list format aligned with corresponding time periods. Our TLQA benchmark, requires both list construction and temporal understanding simultaneously, which to the best of our knowledge has not been explored in prior benchmarks. We investigate the temporal understanding and list construction capabilities of state-of-the-art generative models on TLQA in closed-book and open-domain settings. Our findings reveal significant shortcomings in current models, particularly their inability to provide complete answers and temporally align facts in a closed-book setup and the need to improve retrieval in open-domain setup, providing clear future directions for research on TLQA. The benchmark and code at https://github.com/elixir-research-group/TLQA.
Comparing Learning Paradigms for Egocentric Video Summarization
Venue: arXiv cs.CV 2025
Year: 2025

In this study, we investigate various computer vision paradigms - supervised learning, unsupervised learning, and prompt fine-tuning - by assessing their ability to understand and interpret egocentric video data. Specifically, we examine Shotluck Holmes (state-of-the-art supervised learning), TAC-SUM (state-of-the-art unsupervised learning), and GPT-4o (a prompt fine-tuned pre-trained model), evaluating their effectiveness in video summarization. Our results demonstrate that current state-of-the-art models perform less effectively on first-person videos compared to third-person videos, highlighting the need for further advancements in the egocentric video domain. Notably, a prompt fine-tuned general-purpose GPT-4o model outperforms these specialized models, emphasizing the limitations of existing approaches in adapting to the unique challenges of first-person perspectives. Although our evaluation is conducted on a small subset of egocentric videos from the Ego-Exo4D dataset due to resource constraints, the primary objective of this research is to provide a comprehensive proof-of-concept analysis aimed at advancing the application of computer vision techniques to first-person videos. By exploring novel methodologies and evaluating their potential, we aim to contribute to the ongoing development of models capable of effectively processing and interpreting egocentric perspectives.
Exploring the Structure of AI-Induced Language Change in Scientific English
Venue: arXiv cs.CL 2025
Year: 2025

Scientific English has undergone rapid and unprecedented changes in recent years, with words such as "delve," "intricate," and "crucial" showing significant spikes in frequency since around 2022. These changes are widely attributed to the growing influence of Large Language Models like ChatGPT in the discourse surrounding bias and misalignment. However, apart from changes in frequency, the exact structure of these linguistic shifts has remained unclear. The present study addresses this and investigates whether these changes involve the replacement of synonyms by suddenly 'spiking words,' for example, "crucial" replacing "essential" and "key," or whether they reflect broader semantic and pragmatic qualifications. To further investigate structural changes, we include part of speech tagging in our analysis to quantify linguistic shifts over grammatical categories and differentiate between word forms, like "potential" as a noun vs. as an adjective. We systematically analyze synonym groups for widely discussed 'spiking words' based on frequency trends in scientific abstracts from PubMed. We find that entire semantic clusters often shift together, with most or all words in a group increasing in usage. This pattern suggests that changes induced by Large Language Models are primarily semantic and pragmatic rather than purely lexical. Notably, the adjective "important" shows a significant decline, which prompted us to systematically analyze decreasing lexical items. Our analysis of "collapsing" words reveals a more complex picture, which is consistent with organic language change and contrasts with the patterns of the abrupt spikes. These insights into the structure of language change contribute to our understanding of how language technology continues to shape human language.
Few-Shot Segmentation of Historical Maps via Linear Probing of Vision Foundation Models
Venue: arXiv cs.CV 2025
Year: 2025

As rich sources of history, maps provide crucial insights into historical changes, yet their diverse visual representations and limited annotated data pose significant challenges for automated processing. We propose a simple yet effective approach for few-shot segmentation of historical maps, leveraging the rich semantic embeddings of large vision foundation models combined with parameter-efficient fine-tuning. Our method outperforms the state-of-the-art on the Siegfried benchmark dataset in vineyard and railway segmentation, achieving +5% and +13% relative improvements in mIoU in 10-shot scenarios and around +20% in the more challenging 5-shot setting. Additionally, it demonstrates strong performance on the ICDAR 2021 competition dataset, attaining a mean PQ of 67.3% for building block segmentation, despite not being optimized for this shape-sensitive metric, underscoring its generalizability. Notably, our approach maintains high performance even in extremely low-data regimes (10- & 5-shot), while requiring only 689k trainable parameters - just 0.21% of the total model size. Our approach enables precise segmentation of diverse historical maps while drastically reducing the need for manual annotations, advancing automated processing and analysis in the field. Our implementation is publicly available at: https://github.com/RafaelSterzinger/few-shot-map-segmentation.
TaleForge: Interactive Multimodal System for Personalized Story Creation
Venue: arXiv cs.CV 2025
Year: 2025

Storytelling is a deeply personal and creative process, yet existing methods often treat users as passive consumers, offering generic plots with limited personalization. This undermines engagement and immersion, especially where individual style or appearance is crucial. We introduce TaleForge, a personalized story-generation system that integrates large language models (LLMs) and text-to-image diffusion to embed users' facial images within both narratives and illustrations. TaleForge features three interconnected modules: Story Generation, where LLMs create narratives and character descriptions from user prompts; Personalized Image Generation, merging users' faces and outfit choices into character illustrations; and Background Generation, creating scene backdrops that incorporate personalized characters. A user study demonstrated heightened engagement and ownership when individuals appeared as protagonists. Participants praised the system's real-time previews and intuitive controls, though they requested finer narrative editing tools. TaleForge advances multimodal storytelling by aligning personalized text and imagery to create immersive, user-centric experiences.
The Cost of Avoiding Backpropagation
Venue: arXiv cs.LG 2025
Year: 2025

Forward-mode automatic differentiation (FmAD) and zero-order (ZO) optimization have been proposed as memory-efficient alternatives to backpropagation (BP) for gradient computation, especially in low-resource settings. However, their practical benefits remain unclear due to two key gaps: a lack of comparison against memory-efficient BP variants, such as activation checkpointing, and a lack of a unified theoretical analysis. This work presents a comprehensive theoretical and empirical comparison of BP, FmAD, and ZO methods. Our theoretical analysis shows that while FmAD, and ZO can reduce memory usage, they incur significant costs in accuracy, convergence speed, and computation compared to BP with checkpointing. These drawbacks worsen with larger models or constrained perturbation budgets. Empirical experiments on large language and vision-language models show that BP with checkpointing outperforms FmAD and ZO variants, including those enhanced with variance reduction, achieving up to 31.1% higher accuracy, 34.8% faster convergence, and 3.8x fewer computations at comparable memory usage. Our results highlight fundamental limitations of FmAD and ZO, and reaffirm BP with checkpointing as the most effective strategy for model training under memory-constrained settings. Our code is available at https://github.com/Astuary/The_Cost_of_Avoiding_Backpropagation.
PARSI: Persian Authorship Recognition via Stylometric Integration
Venue: arXiv cs.CL 2025
Year: 2025

The intricate linguistic, stylistic, and metrical aspects of Persian classical poetry pose a challenge for computational authorship attribution. In this work, we present a versatile framework to determine authorship among 67 prominent poets. We employ a multi-input neural framework consisting of a transformer-based language encoder complemented by features addressing the semantic, stylometric, and metrical dimensions of Persian poetry. Our feature set encompasses 100-dimensional Word2Vec embeddings, seven stylometric measures, and categorical encodings of poetic form and meter. We compiled a vast corpus of 647,653 verses of the Ganjoor digital collection, validating the data through strict preprocessing and author verification while preserving poem-level splitting to prevent overlap. This work employs verse-level classification and majority and weighted voting schemes in evaluation, revealing that weighted voting yields 71% accuracy. We further investigate threshold-based decision filtering, allowing the model to generate highly confident predictions, achieving 97% accuracy at a 0.9 threshold, though at lower coverage. Our work focuses on the integration of deep representational forms with domain-specific features for improved authorship attribution. The results illustrate the potential of our approach for automated classification and the contribution to stylistic analysis, authorship disputes, and general computational literature research. This research will facilitate further research on multilingual author attribution, style shift, and generative modeling of Persian poetry.
SPADE: Spatial Transcriptomics and Pathology Alignment Using a Mixture of Data Experts for an Expressive Latent Space
Venue: arXiv cs.CV 2025
Year: 2025

The rapid growth of digital pathology and advances in self-supervised deep learning have enabled the development of foundational models for various pathology tasks across diverse diseases. While multimodal approaches integrating diverse data sources have emerged, a critical gap remains in the comprehensive integration of whole-slide images (WSIs) with spatial transcriptomics (ST), which is crucial for capturing critical molecular heterogeneity beyond standard hematoxylin & eosin (H&E) staining. We introduce SPADE, a foundation model that integrates histopathology with ST data to guide image representation learning within a unified framework, in effect creating an ST-informed latent space. SPADE leverages a mixture-of-data experts technique, where experts are created via two-stage imaging feature-space clustering using contrastive learning to learn representations of co-registered WSI patches and gene expression profiles. Pre-trained on the comprehensive HEST-1k dataset, SPADE is evaluated on 20 downstream tasks, demonstrating significantly superior few-shot performance compared to baseline models, highlighting the benefits of integrating morphological and molecular information into one latent space. Code and pretrained weights are available at https://github.com/uclabair/SPADE.
Grounding-Aware Token Pruning: Recovering from Drastic Performance Drops in Visual Grounding Caused by Pruning
Venue: arXiv cs.CV 2025
Year: 2025

Recent Multimodal Large Language Models (MLLMs) have demonstrated strong performance in visual grounding, establishing themselves as a general interface for various vision-language applications. This progress has driven the development of token pruning methods to mitigate the high computational costs associated with processing numerous visual tokens. However, we observe that pruning significantly weakens the model's grounding ability, leading to incorrect predictions and drastic performance degradation. In Referring Expression Comprehension (REC), for instance, pruning causes the accuracy of LLaVA on the RefCOCO validation set to drop from 56.14% to 15.34%. Our analysis identifies misaligned position IDs after pruning as the primary cause of this degradation, as both the order and value of these IDs are crucial for maintaining performance in grounding tasks. To address this issue, we propose Grounding-Aware Token Pruning (GAP), a simple yet effective adjustment to position IDs that recovers REC accuracy back to 51.42%, which is 90% of the original performance in the without pruning setting, all while requiring no additional training, memory, or computational overhead. Applied to models such as Shikra, MiniGPTv2, and the LLaVA series, our method consistently improves performance across various token pruning strategies.
A Dual-Layered Evaluation of Geopolitical and Cultural Bias in LLMs
Venue: arXiv cs.CL 2025
Year: 2025

As large language models (LLMs) are increasingly deployed across diverse linguistic and cultural contexts, understanding their behavior in both factual and disputable scenarios is essential, especially when their outputs may shape public opinion or reinforce dominant narratives. In this paper, we define two types of bias in LLMs: model bias (bias stemming from model training) and inference bias (bias induced by the language of the query), through a two-phase evaluation. Phase 1 evaluates LLMs on factual questions where a single verifiable answer exists, assessing whether models maintain consistency across different query languages. Phase 2 expands the scope by probing geopolitically sensitive disputes, where responses may reflect culturally embedded or ideologically aligned perspectives. We construct a manually curated dataset spanning both factual and disputable QA, across four languages and question types. The results show that Phase 1 exhibits query language induced alignment, while Phase 2 reflects an interplay between the model's training context and query language. This paper offers a structured framework for evaluating LLM behavior across neutral and sensitive topics, providing insights for future LLM deployment and culturally aware evaluation practices in multilingual contexts.